Behavior segmentation of electricity consumption patterns: A cluster analytical approach

Abstract

Developing effective energy use and management strategies requires the knowledge of determinants and patterns of the electricity usage behavior of different consumers. This paper examines data-driven unsupervised learning schemes to partition the smart meter users into different clusters such that the users in the same cluster have similar consumption patterns represented by their similar daily routines and peak demand time periods. However, it is critical to resolve the problem of high dimensional space in long time series of smart meter data to make the clustering practical. This problem is resolved by time series data mining based on meaningful statistics and behavioral characteristics to emphasize the essential features for reducing the dimensionality and noisiness of the data. Our work involves rigorous data analysis of smart meter data to identify the essential behavioral characteristics and the source of variability in the behavior and analysis of these behavioral characteristics to select the clustering algorithm and its parameters appropriately. These behavioral features will be extracted from each time-series and then used for clustering using a Gaussian mixture modeling​ algorithm. The clustering algorithm is executed on a real-world electricity consumption dataset of 5038 consumers in Slovenia. The classifications obtained with the algorithm are effective when tested visually and by scoring methods of internal indices. The results indicate that the time of day, day of the week, day-to-day variability and seasonality differences that exist in different clusters can be used to formulate new energy management and grid planning strategies.